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Genome sequence of enterohaemorrhagic Escherichia coli O157:H7

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TLDR
It is found that lateral gene transfer is far more extensive than previously anticipated and 1,387 new genes encoded in strain-specific clusters of diverse sizes were found in O157:H7, including candidate virulence factors, alternative metabolic capacities, several prophages and other new functions—all of which could be targets for surveillance.
Abstract
The bacterium Escherichia coli O157:H7 is a worldwide threat to public health and has been implicated in many outbreaks of haemorrhagic colitis, some of which included fatalities caused by haemolytic uraemic syndrome. Close to 75,000 cases of O157:H7 infection are now estimated to occur annually in the United States. The severity of disease, the lack of effective treatment and the potential for large-scale outbreaks from contaminated food supplies have propelled intensive research on the pathogenesis and detection of E. coli O157:H7 (ref. 4). Here we have sequenced the genome of E. coli O157:H7 to identify candidate genes responsible for pathogenesis, to develop better methods of strain detection and to advance our understanding of the evolution of E. coli, through comparison with the genome of the non-pathogenic laboratory strain E. coli K-12 (ref. 5). We find that lateral gene transfer is far more extensive than previously anticipated. In fact, 1,387 new genes encoded in strain-specific clusters of diverse sizes were found in O157:H7. These include candidate virulence factors, alternative metabolic capacities, several prophages and other new functions--all of which could be targets for surveillance.

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Pathogenic Escherichia coli

TL;DR: Few microorganisms are as versatile as Escherichia coli; it can also be a highly versatile, and frequently deadly, pathogen.
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Mauve: multiple alignment of conserved genomic sequence with rearrangements.

TL;DR: This work presents methods for identification and alignment of conserved genomic DNA in the presence of rearrangements and horizontal transfer and evaluated the quality of Mauve alignments and drawn comparison to other methods through extensive simulations of genome evolution.
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progressiveMauve: Multiple Genome Alignment with Gene Gain, Loss and Rearrangement

TL;DR: A new method to align two or more genomes that have undergone rearrangements due to recombination and substantial amounts of segmental gain and loss is described, demonstrating high accuracy in situations where genomes have undergone biologically feasible amounts of genome rearrangement, segmental loss and loss.
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BLAST Ring Image Generator (BRIG) : simple prokaryote genome comparisons

TL;DR: BRIG is a cross-platform application that enables the interactive generation of comparative genomic images via a simple graphical-user interface and will perform all required file parsing and BLAST comparisons automatically.
References
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Journal ArticleDOI

Basic Local Alignment Search Tool

TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.
Journal ArticleDOI

The Complete Genome Sequence of Escherichia coli K-12

TL;DR: The 4,639,221-base pair sequence of Escherichia coli K-12 is presented and reveals ubiquitous as well as narrowly distributed gene families; many families of similar genes within E. coli are also evident.
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Food-related illness and death in the United States.

TL;DR: Overall, foodborne diseases appear to cause more illnesses but fewer deaths than previously estimated.
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Hemorrhagic colitis associated with a rare Escherichia coli serotype

TL;DR: In this article, the authors investigated two outbreaks of an unusual gastrointestinal illness that affected at least 47 people in Oregon and Michigan in February through March and May through June 1982, which was characterized by severe crampy abdominal pain, initially watery diarrhea followed by grossly bloody diarrhea, and little or no fever.
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GeneMark.hmm: New solutions for gene finding

TL;DR: The hmm algorithm presented here was designed to improve the gene prediction quality in terms of finding exact gene boundaries by embedding the GeneMark models into naturally derived hidden Markov model framework with gene boundaries modeled as transitions between hidden states.
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